Description of the effect of the practical management in the characterization of « terroir effect »

Context and purpose of the study. Nitrogen (N) limitation enhances the production of phenolic compounds in grapes due to the downregulation of the flavonoid biosynthesis pathway.

Multi-mineral wine profiling and artificial intelligence: implementing the signatures of each wine to train algorithms to meet the new challenges facing the wine industry. Although their quantity is minimal, minerals are essential elements in the composition of every wine. Their presence is the result of complex interactions between factors such as soil, vines, climate, topography, and viticultural practices, all influenced by the terroir. Each stage of the winemaking process also contributes to shaping the unique mineral and taste profile of each wine, giving each cuvée its distinctive characteristics.

Grapevine yield is a key indicator to assess the impacts of climate change and the relevance of adaptation strategies in a vineyard landscape. At this scale, a yield model should use a number of parameters and input data in relation to the information available and be able to reproduce vineyard management decisions (e.g. soil and canopy management, irrigation). In this study, we used data from six experimental sites in Southern France (cv. Syrah) to calibrate a model of grapevine yield limited by water constraint (GraY). Each yield component (bud fertility, number of berries per bunch, berry weight) was calculated as a function of the soil water availability simulated by the WaLIS water balance model at critical phenological phases. The model was then evaluated in 10 grapegrowers’ plots, covering a diversity of biophysical and technical contexts (soil type, canopy size, irrigation, cover crop). We identified three critical periods for yield formation: after flowering on the previous year for the number of bunches and berries, around pre-veraison and post-veraison of the same year for mean berry weight. Yields were simulated with a model efficiency (EF) of 0.62 (NRMSE = 0.28). Bud fertility and number of berries per bunch were more accurately simulated (EF = 0.90 and 0.77, NRMSE = 0.06 and 0.10, respectively) than berry weight (EF = -0.31, NRMSE = 0.17). Model efficiency on the on-farm plots reached 0.71 (NRMSE = 0.37) simulating yields from 1 to 8 kg/plant. The GraY model is an original model estimating grapevine yield evolution on the basis of water availability under future climatic conditions.  It allows to evaluate the effects of various adaptation levers such as planting density, cover crop management, fruit/leaf ratio, shading and irrigation, in various production contexts.

The ‘Erbaluce‘, a grapevine cultivar from which in the Canavese (Piedmont, Italy) different types of white DOC wines are obtained, is traditionally trained on a support structure commonly known as “pergola” having three to five long “cords” which consist of three cordons and canes interlaced together.

The quality of grapevines measured by yield and must density in the northern part of Europe -conditions can be characterized as a type of “cool climate” – vary strongly from year to year and from one production site to another, i.e. différences in must densities can range from 30 to 50 °Oe. An explanation may be changes of weather conditions during critical developmental stages of the grapevines (2, 3, 5). These can be categorized as “macro climatic” influences.